
Language pre-training via self-supervised learning. The GTG model learns the primary skills of understanding and generating natural language on large amounts of raw text. The pre-trained model encodes text as a sequence of symbolic tokens without grounding them in real-world concepts [1]} and generates text by searching word co-occurrence void of meaning [2]}. Therefore, the model might generate fluent responses that are not useful to achieve any specific goals.

Task-grounded pre-training via supervised learning. GTG learns the primary skills of task completion, including tracking dialog belief states and user intents to build internal cognition models, knowledge base lookup (via semantic parsing), and deciding how to respond to complete a task. The pre-trained model grounds language words in perceptions and real-world concepts, and generates responses in terms of causality.

Task-specific fine-tuning via supervised learning and reinforcement learning. GTG learns to adapt itself, in a fully embodied and social context, to complete specific tasks using a hybrid learning framework based on reinforcement learning and machine teaching, which is a form of supervised learning where training samples are generated by human teachers interacting with the system, as detailed in [3]}, so that (1) the responses generated by GTG are grounded in task-specific rules and knowledge base that are encoded into the system via coding and machine learning, (2) the responses are optimized for task completion, and (3) the primary skills are constantly improved task by task so that the bot can adapt to new tasks more easily.

